Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. While the introduction of the attention mechanism benefits the hierarchical BiLSTM model, the hierarchical Transformer, with its inherent attention and positional encoding mechanisms also performs better than flat transformer model. This representation is the output of the last encoder block in the case of Transformer, and the last hidden state in the case of BiLSTM. The methodology employed in these modules has been described next. It uses complex AI algorithms to generate questions. Question generation is the most important part of the teaching-learning process. 2018. The generated question list is printed as output. Using AI and NLP it is possible to generate questions from sentenses or paragraph. As humans, when reading a paragraph, we look for important sentences first and then important keywords in those sentences to find a concept around which a question can be generated. To Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates … Ms marco: A human generated machine reading comprehension dataset. Keep your question short and to the point. If nothing happens, download the GitHub extension for Visual Studio and try again. The context vector ct is fed to the decoder at time step t along with embedded representation of the previous output. 2018. On the MS MARCO dataset, the two LSTM-based models outperform the two Transformer-based models. We propose a general hierarchical architecture for better paragraph representation at the level of words and sentences. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Thus, for paragraph-level question generation, the hierarchical representation of paragraphs is a worthy pursuit. If nothing happens, download GitHub Desktop and try again. comprehension. Du et al. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. We the concatenate forward and backward hidden states of the BiLSTM encoder to obtain the final hidden state representation (ht) at time step t. Representation (ht) is calculated as: In case if the purpose of your research is about language testing, you need to determine what question type you want to generate at first; e.g. We present human evaluation results in Table 3 and Table 4 respectively. Let us assume that the question decoder needs to attend to the source paragraph during the generation process. In reality, however, it often requires the whole paragraph as context in order to generate high quality questions. Rouge: A package for automatic evaluation of summaries. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. We also propose a novel hierarchical BiLSTM model with selective attention, which learns to attend to important sentences and words from the paragraph that are relevant to generate meaningful and fluent questions about the encoded answer. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, (2018) recently proposed a Seq2Seq model for paragraph-level question generation, where they employ a maxout pointer mechanism with a gated self-attention encoder. Here, d is the dimension of the query/key vectors; the dimension of the resulting attention vector would be the number of sentences in the paragraph. In Machine Translation, non-recurrent model such as a Transformer (Vaswani et al., 2017) that does not use convolution or recurrent connection is often expected to perform better. We performed all our experiments on the publicly available SQuAD (Rajpurkar et al., 2016) and MS MARCO (Nguyen et al., 2016) datasets. Based on a set of 90 predefined interaction rules, we check the coarse classes according to the word to word interaction. Random questions can be a wonderful way to begin a writing session each day. We compare QG results of our hierarchical LSTM and hierarchical Transformer with their flat counterparts. Out of the search results, 122 papers were considered relevant after looking at their titles and abstracts. for question generation from text. However, the hierarchical BiLSTM model HierSeq2Seq + AE  achieves best, and significantly better, relevance scores on both datasets. Recently, Zhao et al. Rule based methods (Heilman and Smith, 2010) perform syntactic and semantic analysis of sentences and apply fixed sets of rules to generate questions. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Figure 2), we make use of a Transformer decoder to generate the target question, one token at a time, from left to right. QG at the paragraph level is much less explored and it has remained a challenging problem. The automatic question generation is an important research area which is potentially useful in intelligent tutoring systems, dialogue systems, educational technologies, instructionalgames etc. In this paper, we propose and study two hierarchical models for the task of question generation from paragraphs. 2018. We analyzed quality of questions generated on a) syntactic correctness b) semantic correctness and c) relevance to the given paragraph. download the GitHub extension for Visual Studio. A question type driven framework to diversify visual question python -m textblob.download_corpora Many times all it takes is a simple question to be answered or used to destroy the block that was there. Stroudsburg, PA : ACL , pp. Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Figure 1). We feed the sentence representations ~s to our sentence-level BiLSTM encoder (c.f. Ph.D. thesis, Carnegie Mellon University. As of 2019, Question generation from text has become possible. Ref: Alphabetical list of part-of-speech tags used in the Penn Treebank Project. One straightforward extension to such a model would be to reflect the structure of a paragraph in the design of the encoder. Equipped with different enhancements such as the attention, copy and coverage mechanisms, RNN-based models (Du et al., 2017; Kumar et al., 2018; Song et al., 2018) achieve good results on sentence-level question generation. We postulate that attention to the paragraph benefits from our hierarchical representation, described in Section 3.1. Secondly, it computes an attention vector for the words of each sentence: Firstly, this module attends to paragraph sentences using their keys and the sentence query vector: The input to this encoder are the sentence representations produced by the lower level encoder, which are insensitive to the paragraph context. At the lower level, the encoder first encodes words and produces a sentence-level representation. This encoder produces a sentence-dependent word representation ri,j for each word xi,j in a sentence xi, i.e., ri=\textscWordEnc (xi). The question generation task consists of pairs (X,y) conditioned on an encoded answer z, where X is a paragraph, and y is the target question which needs to be generated with respect to the paragraph.. 2018. Learners have access to learning materials from a wide variety of sources, and these materials are not often accompanied by questions to help guide learning. This set was further reduced to 36 papers after reading the full text of the papers. Our findings also suggest that LSTM outperforms the Transformer in capturing the hierarchical structure. See my Quora answers on question generation: * Can computers make questions? At the higher level, our HPE consists of another encoder to produce paragraph-dependent representation for the sentences. Agarwal, M., Mannem, P.: Automatic Gap-fill Question Generation from Text Books. … Paragraph-level neural question generation with maxout pointer and In this first option, c.f., Figure 1, we use both word-level attention and sentence level attention in a Hierarchical BiLSTM encoder to obtain the hierarchical paragraph representation. structure. We model a paragraph in terms of its constituent sentences, and a sentence in terms of its constituent words.